Speaker identification in courtroom contexts – Part I: Individual listeners compared to forensic voice comparison based on automatic-speaker-recognition technology. (December 2022)

• Record Type: Journal Article
• Authors: Basu, Nabanita; Bali, Agnes S.; Weber, Philip; Rosas-Aguilar, Claudia; Edmond, Gary; Martire, Kristy A.; Morrison, Geoffrey Stewart
• Is Part Of: Forensic science international. Volume 341(2022)
• Publication Date: 2022-12
• Subjects: Admissibility -- Forensic voice comparison -- Likelihood ratio -- Speaker identification -- Validation -- x-vector; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2022.111499
• Languages: English
• ISSNs: 0379-0738

Abstract

Abstract: Expert testimony is only admissible in common law if it will potentially assist the trier of fact to make a decision that they would not be able to make unaided. The present paper addresses the question of whether speaker identification by an individual lay listener (such as a judge) would be more or less accurate than the output of a forensic-voice-comparison system that is based on state-of-the-art automatic-speaker-recognition technology. Listeners listen to and make probabilistic judgements on pairs of recordings reflecting the conditions of the questioned- and known-speaker recordings in an actual case. Reflecting different courtroom contexts, listeners with different language backgrounds are tested: Some are familiar with the language and accent spoken, some are familiar with the language but less familiar with the accent, and others are less familiar with the language. Also reflecting different courtroom contexts: In one condition listeners make judgements based only on listening, and in another condition listeners make judgements based on both listening to the recordings and considering the likelihood-ratio values output by the forensic-voice-comparison system. Graphical Abstract: ga1 Highlights: Forensic voice comparison based on automatic-speaker-recognition technology. Speaker identification by individual lay listeners. Listening only. Listening and considering output of forensic-voice-comparison system. Different degrees of language and accent familiarity.